A Self-Help Guide For Autonomous Systems

  • Michael L. Anderson Franklin & Marshall College
  • Scott Fults University of Maryland
  • Darsana P. Josyula Bowie State University
  • Tim Oates University of Maryland Baltimore County
  • Don Perlis University of Maryland
  • Shomir Wilson University of Maryland
  • Dean Wright University of Maryland Baltimore County

Abstract

Humans learn from their mistakes. When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don’t even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our past and current work on the Meta-Cognitive Loop, a domain-general approach to giving artificial systems the ability to notice, assess, and repair problems. The goal is to make artificial systems more robust and less dependent on their human designers.

Author Biographies

Michael L. Anderson, Franklin & Marshall College

Department of Psychology

Assistant Professor 

Scott Fults, University of Maryland

Department of Computer Science

Research Scientist 

Darsana P. Josyula, Bowie State University

Department of Computer Science

Assistant Professor 

Tim Oates, University of Maryland Baltimore County

Department of Computer Science and Electrical Engineering

Associate Professor 

Don Perlis, University of Maryland

Department of Computer Science

Professor

Shomir Wilson, University of Maryland

Department of Computer Science

Ph.D. Student 

Dean Wright, University of Maryland Baltimore County

Department of Computer Science and Electrical Engineering

Ph.D. Student 

Published
2008-07-10
Section
Articles